Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	124,848
2	South Dakota	117,779
3	Utah	98,197
4	Rhode Island	96,742
5	Wisconsin	95,839
6	Tennessee	95,702
7	Iowa	95,453
8	Nebraska	92,706
9	Arizona	88,433
10	Kansas	87,597
11	Arkansas	86,824
12	Oklahoma	86,269
13	Indiana	85,729
14	Idaho	85,369
15	Alabama	83,822
16	Wyoming	83,435
17	Illinois	82,765
18	Nevada	82,481
19	Montana	82,111
20	Mississippi	81,951
21	Minnesota	78,094
22	Louisiana	76,543
23	New Mexico	75,885
24	Missouri	74,856
25	California	72,318
26	Georgia	71,391
27	South Carolina	70,964
28	Kentucky	70,880
29	Florida	70,652
30	Texas	70,469
31	Alaska	69,067
32	Delaware	68,982
33	Ohio	68,408
34	New Jersey	67,846
35	Massachusetts	65,063
36	Colorado	64,037
37	Connecticut	61,867
38	North Carolina	61,357
39	New York	60,632
40	Pennsylvania	58,369
41	West Virginia	58,249
42	Michigan	57,239
43	Maryland	52,112
44	Virginia	48,332
45	District of Columbia	46,192
46	New Hampshire	39,806
47	Washington	37,517
48	Puerto Rico	37,027
49	Oregon	30,610
50	Maine	23,173
51	Hawaii	16,712
52	Vermont	15,013

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,532
2	Connecticut	1,363
3	Arizona	1,151
4	Kansas	1,106
5	California	1,079
6	Utah	916
7	Texas	867
8	Oklahoma	842
9	Wyoming	839
10	Georgia	812
11	Arkansas	767
12	Massachusetts	757
13	New York	748
14	Kentucky	734
15	South Carolina	702
16	New Jersey	668
17	Delaware	650
18	Louisiana	645
19	Ohio	643
20	North Carolina	636
21	Florida	627
22	New Hampshire	619
23	Alabama	618
24	Tennessee	607
25	West Virginia	591
26	Nevada	573
27	Pennsylvania	566
28	Mississippi	539
29	Nebraska	539
30	Virginia	534
31	Idaho	525
32	Indiana	519
33	Iowa	473
34	New Mexico	470
35	Missouri	463
36	Illinois	460
37	Maine	459
38	Maryland	452
39	Montana	447
40	Wisconsin	444
41	Washington	409
42	District of Columbia	382
43	Michigan	372
44	Colorado	333
45	South Dakota	330
46	Oregon	270
47	Alaska	269
48	Minnesota	223
49	Vermont	214
50	North Dakota	182
51	Puerto Rico	158
52	Hawaii	85

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,270
2	New York	2,043
3	Massachusetts	1,938
4	Rhode Island	1,875
5	Connecticut	1,833
6	South Dakota	1,813
7	North Dakota	1,789
8	Mississippi	1,785
9	Louisiana	1,725
10	Illinois	1,548
11	Arizona	1,468
12	Pennsylvania	1,441
13	Michigan	1,434
14	Arkansas	1,387
15	Indiana	1,361
16	Iowa	1,341
17	New Mexico	1,339
18	Tennessee	1,181
19	District of Columbia	1,177
20	Alabama	1,174
21	Nevada	1,167
22	Kansas	1,151
23	South Carolina	1,148
24	Florida	1,089
25	Texas	1,082
26	Georgia	1,077
27	Missouri	1,065
28	Maryland	1,059
29	Minnesota	1,035
30	Delaware	1,020
31	Montana	1,000
32	Wisconsin	975
33	Nebraska	960
34	West Virginia	932
35	Colorado	929
36	Wyoming	901
37	Idaho	881
38	Ohio	845
39	California	800
40	Kentucky	744
41	North Carolina	743
42	Oklahoma	719
43	New Hampshire	650
44	Virginia	650
45	Puerto Rico	519
46	Washington	513
47	Utah	451
48	Oregon	406
49	Maine	337
50	Alaska	296
51	Vermont	253
52	Hawaii	218

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	28
2	Arizona	25
3	Kansas	23
4	Rhode Island	22
5	Connecticut	19
6	Wyoming	19
7	Pennsylvania	17
8	Tennessee	17
9	Mississippi	16
10	West Virginia	16
11	Arkansas	15
12	California	13
13	Nevada	13
14	Missouri	12
15	Georgia	10
16	Louisiana	10
17	Massachusetts	10
18	New Jersey	10
19	Iowa	9
20	Nebraska	9
21	New Mexico	9
22	New York	9
23	Texas	9
24	Illinois	8
25	Indiana	8
26	Oregon	8
27	Delaware	7
28	Florida	7
29	Idaho	7
30	Kentucky	7
31	Maryland	7
32	Ohio	7
33	Oklahoma	7
34	South Dakota	7
35	District of Columbia	6
36	Michigan	6
37	South Carolina	6
38	Virginia	6
39	Wisconsin	6
40	Maine	5
41	North Carolina	5
42	Utah	5
43	Washington	5
44	Colorado	4
45	Montana	4
46	Minnesota	3
47	New Hampshire	3
48	Puerto Rico	3
49	North Dakota	1
50	Vermont	1
51	Alaska	0
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	278,502	1	99
Dewey	South Dakota	229,633	2	99
Lincoln	Arkansas	223,741	3	99
Norton	Kansas	222,347	4	99
Bent	Colorado	222,342	5	99
Davidson	Tennessee	111,506	261	91
Richland	South Carolina	72,073	1612	48
Orange	California	66,546	1894	39
York	South Carolina	65,233	1950	37
Pierce	Washington	34,644	2863	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Grant	Nebraska	6,421	4	99
Iron	Wisconsin	6,330	5	99
Davidson	Tennessee	931	1846	41
Richland	South Carolina	861	1951	37
York	South Carolina	701	2206	29
Orange	California	686	2230	29
Pierce	Washington	448	2601	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.